A sequence to sequence model comprises of two RNNs with additional encoder and decoder modules. The encoder routes the input data, whereas the decoder routes the output. The encoder and decoder work concurrently, either using the same parameters or different ones [1]}. This model, conflicting with the actual RNN, is primarily applicable in those cases where the length of the input data is equal to the length of the output data [2]}. Though they own similar assistance and curbs to the RNN, these models are mostly applied in chatbots, machine translations, and question answering systems. Fig. REF  shows the architecture of the sequence to sequence model comprising an encoder and a decoder. Encoder and a decoder are two independent essential neural network models integrated into a massive network to create an output representation sequence. Encoder and decoder comprise two RNNs that act as an encoder and a decoder pair. The encoder receives an input of variable length and maps it to a fixed-length vector, then sends it to the decoder that maps the fixed-length vector back to a variable-length as a target sequence to produce output. This process is accomplished by utilizing the RNN network inside the encoder and decoder.
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